Source of the Data:
It’s taken from Open Government Data (OGD) Platform India link is below here https://data.gov.in/catalog/district-wise-crimes-committed-against-women Reference URL of Resource https://ncrb.gov.in/
Original Authors/Contributors of the Data:
- Ministry of Home Affairs
- Department of States
- National Crime Records Bureau (NCRB)
Data Uploaded Date
- Published on: 24/10/2017
- Updated on: 12/03/2020
Purpose of the Data
To know and analysis various factor of Crimes committed against Women during 2015. To analysis crime against Women of every State of India .To visualise different-different type of crimes (like Rape, Dowry Deaths, Kidnapping, Cruelty by Husband or his Relatives… etc) according to State and district. By all this To find which states are unsafe for women and which states are safe.
Data Description
The data refers to district wise details on crimes against women during the years. The nature of such crimes includes Rape, Kidnapping and Abduction, Dowry Death, Assault on women with intent to outrage her modesty, Insult to modesty of Women, Cruelty by Husband or his Relatives and Importation of Girls. This data is collected to study one of the major issues of the country namely “crime against Women”. Various analysis of the data is done for strategic planning and making policies to prevent crime against Women.
data = read.csv("C:/Users/verma/Documents/DESKTOP2.0/project/District-wise_Crimes_committed_against_Women_2015.csv")
colnames(data)
## [1] "State..UT"
## [2] "Sl..No."
## [3] "District..Area"
## [4] "Year"
## [5] "Rape"
## [6] "Attempt.to.commit.Rape"
## [7] "Kidnapping...Abduction_Total"
## [8] "Dowry.Deaths"
## [9] "Assault.on.Women.with.intent.to.outrage.her.Modesty_Total"
## [10] "Insult.to.the.Modesty.of.Women_Total"
## [11] "Cruelty.by.Husband.or.his.Relatives"
## [12] "Importation.of.Girls.from.Foreign.Country"
## [13] "Abetment.of.Suicides.of.Women"
## [14] "Dowry.Prohibition.Act..1961"
## [15] "Indecent.Representation.of.Women..P..Act..1986"
## [16] "Protection.of.Children.from.Sexual.Offences.Act"
## [17] "Protection.of.Women.from.Domestic.Violence.Act..2005"
## [18] "Immoral.Traffic.Prevention.Act"
## [19] "Total.Crimes.against.Women"
library(ggplot2)
library(moments)
library(gridExtra)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following object is masked from 'package:gridExtra':
##
## combine
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
| Column Name | Description of column | Type (character, integer, numeric, logical) |
|---|---|---|
| State/ UT | State/ UT Names | Character |
| Sl. No. | Serial no. of District in States | Integer |
| District/ Area | District/ Area of States | Character |
| Year | Only Year is 2015 | Integer |
| Rape | No. of Rape Cases | Integer |
| Attempt to commit Rape | No. of Attempt to commit Rape | Integer |
| Kidnapping & Abduction_Total | No. of Kidnapping & Abduction Cases | Integer |
| Dowry Deaths | No. Dowry Deaths | Integer |
| Assault on Women with intent to outrage her Modesty_Total | No. of Assault on Women with intent to outrage her Modesty Cases | Integer |
| Insult to the Modesty of Women_Total | No. of Insult to the Modesty of Women Cases | Integer |
| Cruelty by Husband or his Relatives | No. of Cruelty by Husband/his Relatives Cases | Integer |
| Importation of Girls from Foreign Country | Cases of Importation of Girls from Foreign Country | Integer |
| Abetment of Suicides of Women | Cases of Abetment of Suicides of Women | Integer |
| Dowry Prohibition Act, 1961 | Cases under Dowry Prohibition Act(1961) | Integer |
| Indecent Representation of Women (P) Act, 1986 | Cases under Indecent Representation of Women Act(1986) | Integer |
| Protection of Children from Sexual Offences Act | Cases under Protection of Children from Sexual Offences Act | Integer |
| Protection of Women from Domestic Violence Act, 2005 | Cases under Protection of Women from Domestic Violence Act(2005) | Integer |
| Immoral Traffic Prevention Act | Cases under Immoral Traffic Prevention Act | Integer |
| Total Crimes against Women | Total Crimes Cases of each district in states | Integer |
StatesRep = c(data$State..UT)
States = c()
for(i in 1:length(data$State..UT))
{
if(data$District..Area[i] == "Total District(s)"){
States = data$State..UT[data$District..Area == "Total District(s)"]
States
}
}
States
## [1] "Andhra Pradesh" "Arunachal Pradesh" "Assam"
## [4] "Bihar" "Chhattisgarh" "Goa"
## [7] "Gujarat" "Haryana" "Himachal Pradesh"
## [10] "Jammu & Kashmir" "Jharkhand" "Karnataka"
## [13] "Kerala" "Madhya Pradesh" "Maharashtra"
## [16] "Manipur" "Meghalaya" "Mizoram"
## [19] "Nagaland" "Odisha" "Punjab"
## [22] "Rajasthan" "Sikkim" "Tamil Nadu"
## [25] "Telangana" "Tripura" "Uttar Pradesh"
## [28] "Uttarakhand" "West Bengal" "A & N Islands"
## [31] "Chandigarh" "D&N Haveli" "Daman & Diu"
## [34] "Delhi UT" "Lakshadweep" "Puducherry"
length(States)
## [1] 36
RapeCaseS = data$Rape[data$District..Area == "Total District(s)"]
my_bar = barplot(RapeCaseS, names.arg = States, las = 2, ylim = c(0,5000), cex.names = 0.6, col= rainbow(36), main = "Rape Cases in States(in number)")
text(my_bar,RapeCaseS+150, paste( RapeCaseS, sep="") ,cex=0.6)
plot_ly(values = RapeCaseS ,labels = States ,type = 'pie', title = "Rape Cases in States" )
Minimun =min(RapeCaseS)
q_1 =quantile(RapeCaseS)[2]
Median =median(RapeCaseS)
Mean =mean(RapeCaseS)
q_3 =quantile(RapeCaseS)[4]
Max =max(RapeCaseS)
Var =var(RapeCaseS)
Skewness =skewness(RapeCaseS)
Kurtosis =kurtosis(RapeCaseS)
# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_RapeCaseS=c(Minimun ,q_1 ,Median, Mean, q_3 ,Max ,Skewness ,Kurtosis ,Var )
#table_age
df1=data.frame(Statistic, Value =value_RapeCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
## Statistic Value
## 1 Minimum 0.000000e+00
## 2 1st Quartile 6.775000e+01
## 3 Median 4.620000e+02
## 4 Mean 9.625278e+02
## 5 3rd Quartile 1.213250e+03
## 6 Max 4.391000e+03
## 7 Varience 1.521205e+00
## 8 Skewness 4.456413e+00
## 9 Kurtosis 1.467639e+06
TC = which(data$District..Area == "Total District(s)")
data2 = data %>% filter(!row_number() %in% TC)
CommitRapeCaseS = data$Attempt.to.commit.Rape[data$District..Area == "Total District(s)"]
my_bar = barplot(CommitRapeCaseS, names.arg = States, las =2, col= rainbow(36),cex.names = 0.6, ylim = c(0, 2000))
text(my_bar,CommitRapeCaseS+100, paste( CommitRapeCaseS, sep="") ,cex=0.6)
plot_ly(values = CommitRapeCaseS ,labels = States ,type = 'pie', title = "Attempt to Rape Cases in states" )
Minimun =min(CommitRapeCaseS)
q_1 =quantile(CommitRapeCaseS)[2]
Median =median(CommitRapeCaseS)
Mean =mean(CommitRapeCaseS)
q_3 =quantile(CommitRapeCaseS)[4]
Max =max(CommitRapeCaseS)
Var =var(CommitRapeCaseS)
Skewness =skewness(CommitRapeCaseS)
Kurtosis =kurtosis(CommitRapeCaseS)
# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_CommitRapeCaseS =c(Minimun ,q_1 ,Median ,Mean ,q_3 ,Max ,Skewness ,Kurtosis ,Var )
#table_age
df1=data.frame(Statistic, Value = value_CommitRapeCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
## Statistic Value
## 1 Minimum 0.000000
## 2 1st Quartile 1.000000
## 3 Median 20.000000
## 4 Mean 123.250000
## 5 3rd Quartile 110.750000
## 6 Max 1551.000000
## 7 Varience 3.935056
## 8 Skewness 19.909701
## 9 Kurtosis 78736.764286
kidnappingCaseS = data$Kidnapping...Abduction_Total[data$District..Area == "Total District(s)"]
my_bar = barplot(kidnappingCaseS, names.arg = States, las =2, col= rainbow(36),cex.names = 0.6, ylim = c(0,12000), main = "Kidnapping Cases in states(number)")
text(my_bar,kidnappingCaseS+400, paste( kidnappingCaseS, sep="") ,cex=0.5)
plot_ly(values = kidnappingCaseS ,labels = States ,type = 'pie', title = "Kidnapping Cases in states" )
Minimun_temp=min(kidnappingCaseS)
q_1_temp=quantile(kidnappingCaseS)[2]
Median_temp=median(kidnappingCaseS)
Mean_temp=mean(kidnappingCaseS)
q_3_temp=quantile(kidnappingCaseS)[4]
Max_temp=max(kidnappingCaseS)
Var_temp=var(kidnappingCaseS)
Skewness_temp=skewness(kidnappingCaseS)
Kurtosis_temp=kurtosis(kidnappingCaseS)
# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_kidnappingCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_kidnappingCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
## Statistic Value
## 1 Minimum 0.000000e+00
## 2 1st Quartile 6.700000e+01
## 3 Median 6.660000e+02
## 4 Mean 1.646583e+03
## 5 3rd Quartile 2.398750e+03
## 6 Max 1.013500e+04
## 7 Varience 1.827504e+00
## 8 Skewness 6.517332e+00
## 9 Kurtosis 5.162887e+06
DowryDeathsCaseS = data$Dowry.Deaths[data$District..Area == "Total District(s)"]
my_bar = barplot(DowryDeathsCaseS, names.arg = States, las =2, col= rainbow(36),ylim = c(0,2500) ,cex.names = 0.6, main = "Dowry Deaths Cases in states(in number)")
text(my_bar,DowryDeathsCaseS+120, paste( DowryDeathsCaseS, sep="") ,cex=0.6)
plot_ly(values = DowryDeathsCaseS ,labels = States ,type = 'pie', title = "Dowry Deaths Cases in states" )
Minimun_temp=min(DowryDeathsCaseS)
q_1_temp=quantile(DowryDeathsCaseS)[2]
Median_temp=median(DowryDeathsCaseS)
Mean_temp=mean(DowryDeathsCaseS)
q_3_temp=quantile(DowryDeathsCaseS)[4]
Max_temp=max(DowryDeathsCaseS)
Var_temp=var(DowryDeathsCaseS)
Skewness_temp=skewness(DowryDeathsCaseS)
Kurtosis_temp=kurtosis(DowryDeathsCaseS)
# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_DowryDeathsCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_DowryDeathsCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
## Statistic Value
## 1 Minimum 0.00000
## 2 1st Quartile 1.00000
## 3 Median 41.00000
## 4 Mean 212.05556
## 5 3rd Quartile 256.00000
## 6 Max 2335.00000
## 7 Varience 3.62952
## 8 Skewness 17.27124
## 9 Kurtosis 189874.28254
* No of Dowry Death Cases is High in Uttar Pradesh with 2335. 30.6 percentage of Dowry Death Cases is happend in Uttar Pradesh, 2nd highest cases in Bihar(15.1 % of Kidnapping Cases) and 3rd highest cases in Madhya Pradesh(8.7 % of Kidnapping Cases)
* The Lakshadweep, Arunachal Pradesh, Goa, Manipur, Mizorm, Nagaland, A & N Islands and Daman & Diu has 0 no of Dowry Death Cases.
AssaultCaseS = data$Assault.on.Women.with.intent.to.outrage.her.Modesty_Total[data$District..Area == "Total District(s)"]
my_bar= barplot(AssaultCaseS, names.arg = States, las =2, col= rainbow(36),cex.names = 0.6, ylim = c(0,13000), main = "Assault on Women Cases in states(number)" )
text(my_bar,AssaultCaseS+400, paste( AssaultCaseS, sep="") ,cex=0.6)
plot_ly(values = AssaultCaseS ,labels = States ,type = 'pie', title = "Assault on Women Cases in states" )
Minimun_temp=min(AssaultCaseS)
q_1_temp=quantile(AssaultCaseS)[2]
Median_temp=median(AssaultCaseS)
Mean_temp=mean(AssaultCaseS)
q_3_temp=quantile(AssaultCaseS)[4]
Max_temp=max(AssaultCaseS)
Var_temp=var(AssaultCaseS)
Skewness_temp=skewness(AssaultCaseS)
Kurtosis_temp=kurtosis(AssaultCaseS)
# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_AssaultCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_AssaultCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
## Statistic Value
## 1 Minimum 5.000000e+00
## 2 1st Quartile 8.200000e+01
## 3 Median 9.835000e+02
## 4 Mean 2.289500e+03
## 5 3rd Quartile 4.373750e+03
## 6 Max 1.171300e+04
## 7 Varience 1.369116e+00
## 8 Skewness 4.274321e+00
## 9 Kurtosis 8.690933e+06
InsultCaseS = data$Insult.to.the.Modesty.of.Women_Total[data$District..Area == "Total District(s)"]
my_bar = barplot(InsultCaseS, names.arg = States, las =2, col= rainbow(36),cex.names= 0.6, ylim = c(0,2500), main = "Insult to the Modesty of Women Cases in states" )
text(my_bar, InsultCaseS+150, paste( InsultCaseS, sep="") ,cex=0.6)
plot_ly(values = InsultCaseS ,labels = States ,type = 'pie', title = "Insult to the Modesty of Women Cases in states" )
Minimun_temp=min(AssaultCaseS)
q_1_temp=quantile(AssaultCaseS)[2]
Median_temp=median(AssaultCaseS)
Mean_temp=mean(AssaultCaseS)
q_3_temp=quantile(AssaultCaseS)[4]
Max_temp=max(AssaultCaseS)
Var_temp=var(AssaultCaseS)
Skewness_temp=skewness(AssaultCaseS)
Kurtosis_temp=kurtosis(AssaultCaseS)
# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_AssaultCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_AssaultCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
## Statistic Value
## 1 Minimum 5.000000e+00
## 2 1st Quartile 8.200000e+01
## 3 Median 9.835000e+02
## 4 Mean 2.289500e+03
## 5 3rd Quartile 4.373750e+03
## 6 Max 1.171300e+04
## 7 Varience 1.369116e+00
## 8 Skewness 4.274321e+00
## 9 Kurtosis 8.690933e+06
CrueltyCaseS = data$Cruelty.by.Husband.or.his.Relatives[data$District..Area == "Total District(s)"]
my_bar = barplot(CrueltyCaseS, names.arg = States, las =2, col= rainbow(36),cex.names = 0.6, ylim = c(0,25000), main = "Cruelty by Relatives Cases in states(in number)" )
text(my_bar,CrueltyCaseS+600, paste( CrueltyCaseS, sep="") ,cex=0.6)
plot_ly(values = CrueltyCaseS ,labels = States ,type = 'pie', title = "Cruelty by Relatives Cases in states" )
Minimun_temp=min(CrueltyCaseS)
q_1_temp=quantile(CrueltyCaseS)[2]
Median_temp=median(CrueltyCaseS)
Mean_temp=mean(CrueltyCaseS)
q_3_temp=quantile(CrueltyCaseS)[4]
Max_temp=max(CrueltyCaseS)
Var_temp=var(CrueltyCaseS)
Skewness_temp=skewness(CrueltyCaseS)
Kurtosis_temp=kurtosis(CrueltyCaseS)
# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_CrueltyCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_CrueltyCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
## Statistic Value
## 1 Minimum 2.000000e+00
## 2 1st Quartile 3.400000e+01
## 3 Median 1.101500e+03
## 4 Mean 3.150083e+03
## 5 3rd Quartile 3.877250e+03
## 6 Max 2.016300e+04
## 7 Varience 2.027406e+00
## 8 Skewness 7.150055e+00
## 9 Kurtosis 2.099172e+07
ImportationCaseS = data$Importation.of.Girls.from.Foreign.Country[data$District..Area == "Total District(s)"]
my_bar = barplot(ImportationCaseS, names.arg = States, las =2, col= rainbow(36),cex.names = 0.6, ylim = c(0,6), main = "Importation of Girls from Foreign Country Cases in states" )
text(my_bar,ImportationCaseS+0.2, paste( ImportationCaseS, sep="") ,cex=0.6)
plot_ly(values = ImportationCaseS ,labels = States ,type = 'pie', title = "Importation of Girls from Foreign Country Cases in states" )
Minimun_temp=min(ImportationCaseS)
q_1_temp=quantile(ImportationCaseS)[2]
Median_temp=median(ImportationCaseS)
Mean_temp=mean(ImportationCaseS)
q_3_temp=quantile(ImportationCaseS)[4]
Max_temp=max(ImportationCaseS)
Var_temp=var(ImportationCaseS)
Skewness_temp=skewness(ImportationCaseS)
Kurtosis_temp=kurtosis(ImportationCaseS)
# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_ImportationCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_ImportationCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
## Statistic Value
## 1 Minimum 0.0000000
## 2 1st Quartile 0.0000000
## 3 Median 0.0000000
## 4 Mean 0.1666667
## 5 3rd Quartile 0.0000000
## 6 Max 4.0000000
## 7 Varience 4.5158676
## 8 Skewness 22.6620499
## 9 Kurtosis 0.5428571
suicidesCaseS = data$Abetment.of.Suicides.of.Women[data$District..Area == "Total District(s)"]
my_bar = barplot(suicidesCaseS, names.arg = States, las =2, col= rainbow(36),cex.names = 0.6, ylim = c(0,800), main = "Abetment of Suicide of Women Cases in states" )
text(my_bar,suicidesCaseS+40, paste( suicidesCaseS, sep="") ,cex=0.6)
plot_ly(values = suicidesCaseS ,labels = States ,type = 'pie', title = "Abetment of Suicide of Women Cases in states" )
Minimun_temp=min(suicidesCaseS)
q_1_temp=quantile(suicidesCaseS)[2]
Median_temp=median(suicidesCaseS)
Mean_temp=mean(suicidesCaseS)
q_3_temp=quantile(suicidesCaseS)[4]
Max_temp=max(suicidesCaseS)
Var_temp=var(suicidesCaseS)
Skewness_temp=skewness(suicidesCaseS)
Kurtosis_temp=kurtosis(suicidesCaseS)
# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_suicidesCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_suicidesCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
## Statistic Value
## 1 Minimum 0.000000
## 2 1st Quartile 0.000000
## 3 Median 5.000000
## 4 Mean 112.777778
## 5 3rd Quartile 146.000000
## 6 Max 702.000000
## 7 Varience 1.896123
## 8 Skewness 5.719625
## 9 Kurtosis 34285.777778
DowryCaseS = data$Dowry.Prohibition.Act..1961[data$District..Area == "Total District(s)"]
my_bar = barplot(DowryCaseS, names.arg = States, las =2, col= rainbow(36),cex.names = 0.6, ylim = c(0,3000), main=" Dowry Prohibition Act(1961) Cases in states" )
text(my_bar,DowryCaseS+150, paste( DowryCaseS, sep="") ,cex=0.5)
plot_ly(values = DowryCaseS ,labels = States ,type = 'pie', title = " Dowry Prohibition Act(1961) Cases in states" )
Minimun_temp=min(DowryCaseS)
q_1_temp=quantile(DowryCaseS)[2]
Median_temp=median(DowryCaseS)
Mean_temp=mean(DowryCaseS)
q_3_temp=quantile(DowryCaseS)[4]
Max_temp=max(DowryCaseS)
Var_temp=var(DowryCaseS)
Skewness_temp=skewness(DowryCaseS)
Kurtosis_temp=kurtosis(DowryCaseS)
# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_DowryCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_DowryCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
## Statistic Value
## 1 Minimum 0.000000e+00
## 2 1st Quartile 0.000000e+00
## 3 Median 6.000000e+00
## 4 Mean 2.748333e+02
## 5 3rd Quartile 4.700000e+01
## 6 Max 2.766000e+03
## 7 Varience 2.498868e+00
## 8 Skewness 8.319575e+00
## 9 Kurtosis 4.246521e+05
IndecentCaseS = data$Indecent.Representation.of.Women..P..Act..1986[data$District..Area == "Total District(s)"]
my_bar = barplot(IndecentCaseS, names.arg = States, las =2, col= rainbow(36),cex.names= 0.6, ylim = c(0,15), main= " Indecent Representation of Women Act(1986) Cases in states" )
text(my_bar,IndecentCaseS+0.8, paste(IndecentCaseS, sep="") ,cex=0.6)
plot_ly(values = IndecentCaseS ,labels = States ,type = 'pie', title = " Indecent Representation of Women Act(1986) Cases in states")
Minimun_temp=min(IndecentCaseS)
q_1_temp=quantile(IndecentCaseS)[2]
Median_temp=median(IndecentCaseS)
Mean_temp=mean(IndecentCaseS)
q_3_temp=quantile(IndecentCaseS)[4]
Max_temp=max(IndecentCaseS)
Var_temp=var(IndecentCaseS)
Skewness_temp=skewness(IndecentCaseS)
Kurtosis_temp=kurtosis(IndecentCaseS)
# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_IndecentCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_IndecentCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
## Statistic Value
## 1 Minimum 0.000000
## 2 1st Quartile 0.000000
## 3 Median 0.000000
## 4 Mean 1.111111
## 5 3rd Quartile 1.000000
## 6 Max 12.000000
## 7 Varience 2.868491
## 8 Skewness 10.444092
## 9 Kurtosis 7.358730
DomesticViolenceCaseS = data$Protection.of.Women.from.Domestic.Violence.Act..2005[data$District..Area == "Total District(s)"]
my_bar = barplot(DomesticViolenceCaseS, names.arg = States, las =2, col= rainbow(36),cex.names = 0.6, ylim = c(0,200), main = "Cases under Protection of Women from Domestic Violence Act(2005) in states")
text(my_bar,DomesticViolenceCaseS+10, paste( DomesticViolenceCaseS, sep="") ,cex=0.6)
plot_ly(values = DomesticViolenceCaseS ,labels = States ,type = 'pie', title = "Cases under Protection of Women from Domestic Violence Act(2005) in states")
Minimun_temp=min(DomesticViolenceCaseS)
q_1_temp=quantile(DomesticViolenceCaseS)[2]
Median_temp=median(DomesticViolenceCaseS)
Mean_temp=mean(DomesticViolenceCaseS)
q_3_temp=quantile(DomesticViolenceCaseS)[4]
Max_temp=max(DomesticViolenceCaseS)
Var_temp=var(DomesticViolenceCaseS)
Skewness_temp=skewness(DomesticViolenceCaseS)
Kurtosis_temp=kurtosis(DomesticViolenceCaseS)
# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_DomesticViolenceCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_DomesticViolenceCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
## Statistic Value
## 1 Minimum 0.000000
## 2 1st Quartile 0.000000
## 3 Median 0.000000
## 4 Mean 12.805556
## 5 3rd Quartile 4.000000
## 6 Max 161.000000
## 7 Varience 3.223636
## 8 Skewness 12.057937
## 9 Kurtosis 1326.675397
ImmoralCaseS = data$Immoral.Traffic.Prevention.Act[data$District..Area == "Total District(s)"]
my_bar = barplot(ImmoralCaseS, names.arg = States, las =2, col= rainbow(36),cex.names= 0.6, ylim = c(0,600), main= "Cases under Immoral Traffic Prevention Act in states" )
text(my_bar,ImmoralCaseS+20, paste( ImmoralCaseS, sep="") ,cex=0.6)
plot_ly(values = ImmoralCaseS ,labels = States ,type = 'pie', title = "Cases under Immoral Traffic Prevention Act in states")
Minimun_temp=min(ImmoralCaseS)
q_1_temp=quantile(ImmoralCaseS)[2]
Median_temp=median(ImmoralCaseS)
Mean_temp=mean(ImmoralCaseS)
q_3_temp=quantile(ImmoralCaseS)[4]
Max_temp=max(ImmoralCaseS)
Var_temp=var(ImmoralCaseS)
Skewness_temp=skewness(ImmoralCaseS)
Kurtosis_temp=kurtosis(ImmoralCaseS)
# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_ImmoralCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_ImmoralCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
## Statistic Value
## 1 Minimum 0.000000
## 2 1st Quartile 0.000000
## 3 Median 5.000000
## 4 Mean 67.333333
## 5 3rd Quartile 52.500000
## 6 Max 491.000000
## 7 Varience 2.240404
## 8 Skewness 6.945632
## 9 Kurtosis 15837.142857
TotalCrimesCaseS = data$Total.Crimes.against.Women[data$District..Area == "Total District(s)"]
my_bar = barplot(TotalCrimesCaseS, names.arg = States, las =2, col= rainbow(36),cex.names= 0.6, ylim = c(0,40000), main = "Total Crimes against Women in states" )
text(my_bar,TotalCrimesCaseS+1000, paste( TotalCrimesCaseS, sep="") ,cex=0.6)
plot_ly(values = TotalCrimesCaseS ,labels = States ,type = 'pie', title = "Total Crimes against Women in states" )
Minimun_temp=min(TotalCrimesCaseS)
q_1_temp=quantile(TotalCrimesCaseS)[2]
Median_temp=median(TotalCrimesCaseS)
Mean_temp=mean(TotalCrimesCaseS)
q_3_temp=quantile(TotalCrimesCaseS)[4]
Max_temp=max(TotalCrimesCaseS)
Var_temp=var(TotalCrimesCaseS)
Skewness_temp=skewness(TotalCrimesCaseS)
Kurtosis_temp=kurtosis(TotalCrimesCaseS)
# Table of statistics of age feature
Statistic=c("Minimum","1st Quartile","Median","Mean","3rd Quartile","Max","Varience","Skewness","Kurtosis")
value_TotalCrimesCaseS =c(Minimun_temp,q_1_temp,Median_temp,Mean_temp,q_3_temp,Max_temp,Skewness_temp,Kurtosis_temp,Var_temp)
#table_age
df1=data.frame(Statistic, Value = value_TotalCrimesCaseS)
T1=tableGrob(head(df1,n=9),rows = NULL)
df1
## Statistic Value
## 1 Minimum 9.000000e+00
## 2 1st Quartile 3.170000e+02
## 3 Median 5.505500e+03
## 4 Mean 9.094278e+03
## 5 3rd Quartile 1.533400e+04
## 6 Max 3.552700e+04
## 7 Varience 1.079722e+00
## 8 Skewness 2.989564e+00
## 9 Kurtosis 1.170274e+08
s=data2$Rape
n=length(s)
y_dummy=c()
# categorization in 5 part
b = (max(s)-min(s))/6
a = c(min(s)-1,15, 45, 75,105, 150, 6*b)
for( i in 1:n){
for (j in 1:(length(a)-1))
{
if(a[j]<s[i] & s[i]<=a[j+1])
y_dummy[i]=j
}
}
summary(factor(y_dummy))
## 1 2 3 4 5 6
## 281 263 134 64 45 29
Dividing RapeCase columns in 6 Categaries
| Categories | Interval(Cases) |
|---|---|
| 1 | 0 - 15 |
| 2 | 15 - 45 |
| 3 | 45 - 75 |
| 4 | 75 -105 |
| 5 | 105- 150 |
| 6 | 150 - 712 |
dummy_x = function(x){
s=x
n=length(s)
x_dummy=c()
# categorization in 5 part
b = (max(s)-min(s))/6
a = c(min(s)-1,15, 45, 75,105, 150, max(s))
for( i in 1:n){
for (j in 1:(length(a)-1))
{
if(a[j]<s[i] & s[i]<=a[j+1])
x_dummy[i]=j
x_dummy
}
}
x_dummy
}
Dividing RapeCase columns in 6 Categaries
| Categories | Interval(Cases) |
|---|---|
| 1 | 0 - 15 |
| 2 | 15 - 45 |
| 3 | 45 - 75 |
| 4 | 75 -105 |
| 5 | 105- 150 |
| 6 | 150 - max(columns) |
conti = function(x){
y = y_dummy
O_ij = table(y, x)
u_i = rowSums(O_ij)
v_j = colSums(O_ij)
E_ij = outer(u_i, v_j)/sum(O_ij)
z=sum(((O_ij - E_ij)^2)/E_ij)
message("Measure of Association = ",z)
print(O_ij)
print(E_ij)
# To check any measure of associtiom
df = (dim(O_ij)[1]-1)*(dim(O_ij)[2] - 1); df
p =1- pchisq(z, df )
message("there P value is = ",p)
if(p >= 0.05){
print("There is no Association, Which mean x and y are Independent.")
}
else{
print("There is Association, Which mean X and Y are Dependent.")
}
}
conti(dummy_x(data2$Attempt.to.commit.Rape))
## Measure of Association = 109.609070012231
## x
## y 1 2 3 4 5 6
## 1 278 3 0 0 0 0
## 2 239 23 1 0 0 0
## 3 114 13 2 2 2 1
## 4 54 10 0 0 0 0
## 5 40 3 0 0 1 1
## 6 23 1 3 1 0 1
## 1 2 3 4 5 6
## 1 257.58333 18.251225 2.0661765 1.0330882 1.0330882 1.0330882
## 2 241.08333 17.082108 1.9338235 0.9669118 0.9669118 0.9669118
## 3 122.83333 8.703431 0.9852941 0.4926471 0.4926471 0.4926471
## 4 58.66667 4.156863 0.4705882 0.2352941 0.2352941 0.2352941
## 5 41.25000 2.922794 0.3308824 0.1654412 0.1654412 0.1654412
## 6 26.58333 1.883578 0.2132353 0.1066176 0.1066176 0.1066176
## there P value is = 1.44084744135853e-12
## [1] "There is Association, Which mean X and Y are Dependent."
conti(dummy_x(data2$Kidnapping...Abduction_Total))
## Measure of Association = 487.925764143553
## x
## y 1 2 3 4 5 6
## 1 180 78 17 5 1 0
## 2 37 81 49 37 35 24
## 3 11 25 36 24 10 28
## 4 5 6 12 6 18 17
## 5 1 3 9 8 10 14
## 6 0 0 2 1 6 20
## 1 2 3 4 5 6
## 1 80.580882 66.462010 43.045343 27.893382 27.549020 35.469363
## 2 75.419118 62.204657 40.287990 26.106618 25.784314 33.197304
## 3 38.426471 31.693627 20.526961 13.301471 13.137255 16.914216
## 4 18.352941 15.137255 9.803922 6.352941 6.274510 8.078431
## 5 12.904412 10.643382 6.893382 4.466912 4.411765 5.680147
## 6 8.316176 6.859069 4.442402 2.878676 2.843137 3.660539
## there P value is = 0
## [1] "There is Association, Which mean X and Y are Dependent."
conti(dummy_x(data2$Dowry.Deaths))
## Measure of Association = 102.19981241732
## x
## y 1 2 3 4
## 1 274 7 0 0
## 2 183 70 9 1
## 3 96 32 3 3
## 4 47 13 3 1
## 5 30 13 2 0
## 6 16 12 1 0
## 1 2 3 4
## 1 222.45833 50.621324 6.1985294 1.7218137
## 2 208.20833 47.378676 5.8014706 1.6115196
## 3 106.08333 24.139706 2.9558824 0.8210784
## 4 50.66667 11.529412 1.4117647 0.3921569
## 5 35.62500 8.106618 0.9926471 0.2757353
## 6 22.95833 5.224265 0.6397059 0.1776961
## there P value is = 4.9960036108132e-15
## [1] "There is Association, Which mean X and Y are Dependent."
conti(dummy_x(data2$Assault.on.Women.with.intent.to.outrage.her.Modesty_Total))
## Measure of Association = 616.492832906999
## x
## y 1 2 3 4 5 6
## 1 177 67 14 8 6 9
## 2 33 75 54 38 32 31
## 3 5 10 26 21 27 45
## 4 3 3 4 5 14 35
## 5 0 0 0 3 3 39
## 6 0 0 1 0 1 27
## 1 2 3 4 5 6
## 1 75.071078 53.376225 34.091912 25.827206 28.582108 64.051471
## 2 70.262255 49.957108 31.908088 24.172794 26.751225 59.948529
## 3 35.799020 25.453431 16.257353 12.316176 13.629902 30.544118
## 4 17.098039 12.156863 7.764706 5.882353 6.509804 14.588235
## 5 12.022059 8.547794 5.459559 4.136029 4.577206 10.257353
## 6 7.747549 5.508578 3.518382 2.665441 2.949755 6.610294
## there P value is = 0
## [1] "There is Association, Which mean X and Y are Dependent."
conti(dummy_x(data2$Insult.to.the.Modesty.of.Women_Total))
## Measure of Association = 208.618007731855
## x
## y 1 2 3 4 5 6
## 1 275 6 0 0 0 0
## 2 249 6 2 2 1 3
## 3 112 17 1 1 2 1
## 4 44 10 4 1 4 1
## 5 31 10 1 0 2 1
## 6 11 7 1 2 3 5
## 1 2 3 4 5 6
## 1 248.62990 19.284314 3.0992647 2.0661765 4.1323529 3.7879902
## 2 232.70343 18.049020 2.9007353 1.9338235 3.8676471 3.5453431
## 3 118.56373 9.196078 1.4779412 0.9852941 1.9705882 1.8063725
## 4 56.62745 4.392157 0.7058824 0.4705882 0.9411765 0.8627451
## 5 39.81618 3.088235 0.4963235 0.3308824 0.6617647 0.6066176
## 6 25.65931 1.990196 0.3198529 0.2132353 0.4264706 0.3909314
## there P value is = 0
## [1] "There is Association, Which mean X and Y are Dependent."
conti(dummy_x(data2$Cruelty.by.Husband.or.his.Relatives))
## Measure of Association = 466.914867473824
## x
## y 1 2 3 4 5 6
## 1 193 46 17 10 6 9
## 2 40 56 44 24 29 70
## 3 6 18 14 12 22 62
## 4 4 2 5 5 10 38
## 5 1 0 5 6 3 30
## 6 0 0 1 0 1 27
## 1 2 3 4 5 6
## 1 84.024510 42.012255 29.615196 19.628676 24.449755 81.269608
## 2 78.642157 39.321078 27.718137 18.371324 22.883578 76.063725
## 3 40.068627 20.034314 14.122549 9.360294 11.659314 38.754902
## 4 19.137255 9.568627 6.745098 4.470588 5.568627 18.509804
## 5 13.455882 6.727941 4.742647 3.143382 3.915441 13.014706
## 6 8.671569 4.335784 3.056373 2.025735 2.523284 8.387255
## there P value is = 0
## [1] "There is Association, Which mean X and Y are Dependent."
conti(dummy_x(data2$Importation.of.Girls.from.Foreign.Country))
## Measure of Association = 0
## x
## y 1
## 1 281
## 2 263
## 3 134
## 4 64
## 5 45
## 6 29
## 1
## 1 281
## 2 263
## 3 134
## 4 64
## 5 45
## 6 29
## there P value is = 1
## [1] "There is no Association, Which mean x and y are Independent."
conti(dummy_x(data2$Abetment.of.Suicides.of.Women))
## Measure of Association = 134.050400384235
## x
## y 1 2 3 4
## 1 278 3 0 0
## 2 246 15 2 0
## 3 116 17 1 0
## 4 52 9 3 0
## 5 30 11 3 1
## 6 18 9 0 2
## 1 2 3 4
## 1 254.82843 22.039216 3.0992647 1.0330882
## 2 238.50490 20.627451 2.9007353 0.9669118
## 3 121.51961 10.509804 1.4779412 0.4926471
## 4 58.03922 5.019608 0.7058824 0.2352941
## 5 40.80882 3.529412 0.4963235 0.1654412
## 6 26.29902 2.274510 0.3198529 0.1066176
## there P value is = 0
## [1] "There is Association, Which mean X and Y are Dependent."
conti(dummy_x(data2$Dowry.Prohibition.Act..1961))
## Measure of Association = 71.9454194969891
## x
## y 1 2 3 4 5 6
## 1 253 22 5 0 1 0
## 2 195 24 21 6 10 7
## 3 120 4 2 2 2 4
## 4 59 0 2 0 0 3
## 5 42 1 0 0 0 2
## 6 28 1 0 0 0 0
## 1 2 3 4 5 6
## 1 240.02083 17.906863 10.330882 2.7549020 4.4767157 5.5098039
## 2 224.64583 16.759804 9.669118 2.5784314 4.1899510 5.1568627
## 3 114.45833 8.539216 4.926471 1.3137255 2.1348039 2.6274510
## 4 54.66667 4.078431 2.352941 0.6274510 1.0196078 1.2549020
## 5 38.43750 2.867647 1.654412 0.4411765 0.7169118 0.8823529
## 6 24.77083 1.848039 1.066176 0.2843137 0.4620098 0.5686275
## there P value is = 1.97045729766199e-06
## [1] "There is Association, Which mean X and Y are Dependent."
conti(dummy_x(data2$Indecent.Representation.of.Women..P..Act..1986))
## Measure of Association = 0
## x
## y 1
## 1 281
## 2 263
## 3 134
## 4 64
## 5 45
## 6 29
## 1
## 1 281
## 2 263
## 3 134
## 4 64
## 5 45
## 6 29
## there P value is = 1
## [1] "There is no Association, Which mean x and y are Independent."
conti(dummy_x(data2$Protection.of.Women.from.Domestic.Violence.Act..2005))
## Measure of Association = 21.8575126018189
## x
## y 1 2 4 6
## 1 281 0 0 0
## 2 261 1 0 1
## 3 133 1 0 0
## 4 64 0 0 0
## 5 44 0 1 0
## 6 29 0 0 0
## 1 2 4 6
## 1 279.62255 0.68872549 0.34436275 0.34436275
## 2 261.71078 0.64460784 0.32230392 0.32230392
## 3 133.34314 0.32843137 0.16421569 0.16421569
## 4 63.68627 0.15686275 0.07843137 0.07843137
## 5 44.77941 0.11029412 0.05514706 0.05514706
## 6 28.85784 0.07107843 0.03553922 0.03553922
## there P value is = 0.111594884243551
## [1] "There is no Association, Which mean x and y are Independent."
conti(dummy_x(data2$Immoral.Traffic.Prevention.Act))
## Measure of Association = 78.3189716619971
## x
## y 1 2 3 5 6
## 1 276 5 0 0 0
## 2 256 5 1 0 1
## 3 130 3 1 0 0
## 4 59 5 0 0 0
## 5 43 1 0 0 1
## 6 23 2 2 1 1
## 1 2 3 5 6
## 1 271.01348 7.2316176 1.3774510 0.34436275 1.0330882
## 2 253.65319 6.7683824 1.2892157 0.32230392 0.9669118
## 3 129.23775 3.4485294 0.6568627 0.16421569 0.4926471
## 4 61.72549 1.6470588 0.3137255 0.07843137 0.2352941
## 5 43.40074 1.1580882 0.2205882 0.05514706 0.1654412
## 6 27.96936 0.7463235 0.1421569 0.03553922 0.1066176
## there P value is = 7.55926055084899e-09
## [1] "There is Association, Which mean X and Y are Dependent."
conti(dummy_x(data2$Total.Crimes.against.Women))
## Measure of Association = 530.824724342884
## x
## y 1 2 3 4 5 6
## 1 101 50 28 26 28 48
## 2 0 1 7 11 30 214
## 3 0 0 0 0 1 133
## 4 0 0 0 0 0 64
## 5 0 0 0 0 0 45
## 6 0 0 0 0 0 29
## 1 2 3 4 5 6
## 1 34.780637 17.5625 12.052696 12.741422 20.317402 183.54534
## 2 32.552696 16.4375 11.280637 11.925245 19.015931 171.78799
## 3 16.585784 8.3750 5.747549 6.075980 9.688725 87.52696
## 4 7.921569 4.0000 2.745098 2.901961 4.627451 41.80392
## 5 5.569853 2.8125 1.930147 2.040441 3.253676 29.39338
## 6 3.589461 1.8125 1.243873 1.314951 2.096814 18.94240
## there P value is = 0
## [1] "There is Association, Which mean X and Y are Dependent."
COMMENT: